For Pythonic Researchers

Full Python distribution plus an iPython Notebook, with the choice of Julia 0.3.9 and Python 3 Jupyter notebooks; An iPython terminal (JupyterQt Console) if you prefer to work in the command-line; and Spyder,
the Scientific Python Development Environment. Also includes Flask and Astropy.

APLpy (the Astronomical Plotting Library in Python) is a Python module aimed at producing publication-quality plots of astronomical imaging data in FITS format. The module uses Matplotlib, a powerful and interactive plotting package. It is capable of creating output files in several graphical formats, including EPS, PDF, PS, PNG, and SVG.

Bokeh is a Python interactive visualization library for the web. It provides elegant, concise construction of novel graphics in the style of D3.js, with high-performance interactivity over very large or streaming datasets.

AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib, and astropy. It contains a growing library of statistical and machine learning routines for analyzing astronomical data in Python, loaders for several open astronomical datasets, and a large suite of examples of analyzing and visualizing astronomical datasets.

This is the first major release of the Jupyter Notebook since version 4.0 and the "Big Split” of IPython and Jupyter. This release adds some long-awaited features, such as cell tagging, customizing keyboard shortcuts, copying & pasting cells between notebooks, and a more attractive default style for tables. It also comes with many improvements and bug fixes.

A series of videos demonstrating one approach to reproducible data analysis within the Jupyter notebook. Thanks to Jake VanderPlas from UW's e-Science Institute for creating these. Follow Jake's blog Pythonic Perambulations for more tips and tricks.

Jake VanderPlas, Senior Data Scientist and Director of Research recently published the Python Data Science Handbook. This is a detailed guide to the most important Python tools for data science, covering IPython, Jupyter, NumPy, Pandas, Matplotlib, Scikit-Learn, and other tools.

Around this time last year BIDS fellow Katy Huff launched her new book Effective Computation in Physics: Field Guide to Research in Python, written with her co-author Anthony Scopatz and published through O’Reilly. Worth checking out if you haven't already.